【Tech Trend Talk vol.5】社外向け勉強会「教師あり学習とプロダクトへの活用 -(GIG)」GIG inc.
※ 株式会社GIGでは毎月社内勉強会を実施しています
GIG inc.
Good is good.
We provide opportunities to the SEKAI by fusing technology and ideas.
テクノロジーとクリエイティブでセカイをより良くする。小さなチームからスタートした多くの先人達が、世界をより豊かなモノに変革してきました。通信、UX、デバイス、技術の変化と共に世界はまだまだ加速度的に変わります。
Good is good. いいものはいい。GIGは、関わったユーザーやクライアントが前に進める“きっかけ”をつくりつづけます。
「機械学習の教師あり学習をやってみる」を公開
■ お問い合せ
https://giginc.co.jp/contact/
【Tech Trend Talk vol.5】社外向け勉強会「教師あり学習とプロダクトへの活用 -(GIG)」GIG inc.
※ 株式会社GIGでは毎月社内勉強会を実施しています
GIG inc.
Good is good.
We provide opportunities to the SEKAI by fusing technology and ideas.
テクノロジーとクリエイティブでセカイをより良くする。小さなチームからスタートした多くの先人達が、世界をより豊かなモノに変革してきました。通信、UX、デバイス、技術の変化と共に世界はまだまだ加速度的に変わります。
Good is good. いいものはいい。GIGは、関わったユーザーやクライアントが前に進める“きっかけ”をつくりつづけます。
「機械学習の教師あり学習をやってみる」を公開
■ お問い合せ
https://giginc.co.jp/contact/
This document discusses how to make software more green and environmentally friendly. It defines green software as software that is carbon efficient, energy efficient, hardware efficient, and carbon aware. It provides recommendations for various roles within an organization on driving green initiatives, including focusing on efficiency for CxOs, architects, infrastructure engineers, and developers. Examples include optimizing resource usage, using public clouds effectively, prioritizing equipment standardization, and developing applications that can run more efficiently.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...Rakuten Group, Inc.
The document proposes a knowledge-driven query expansion approach for question answering (QA)-based product attribute extraction. It trains QA models using attribute-value pairs from training data as knowledge, while mimicking imperfect knowledge at test time through techniques like knowledge dropout and token mixing. This helps induce better query representations, especially for rare and ambiguous attributes. Experiments on a cleaned product attribute dataset show the proposed approach with all techniques outperforms baseline methods in both macro and micro F1 scores.
This document summarizes Andrew Hajinikitas' work developing Rakuten's private cloud infrastructure. It describes the key components of Rakuten's infrastructure including metal instances, microservers, and GPU servers. It provides details on Rakuten's software stack and their goals to expand managed services. Currently, Rakuten operates 9 data centers in Japan and overseas providing around 30,000 servers to support their ecosystem. Their future plans include extending network self-service, making GPU resources available as a platform service, and improving efficiency through optimized hardware selection.
The document discusses the Travel & Leisure Platform Dept and its responsibilities related to data and platform management. It provides an overview of the technical stack including private/public clouds, databases, containers, and automation/monitoring tools. It then discusses recent projects involving business continuity, containerization, alert integration, and automation. Finally, it describes open roles for a DBA and DevOps position and their responsibilities related to database provisioning, backup/recovery, infrastructure as code, and providing platforms and tools for developers.
This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
Gora API Group technology provides a microservices architecture and APIs for Rakuten's golf course reservation system, improving the user experience and increasing customer loyalty and annual golf rounds. The architecture migrates the monolithic reservation system to microservices using Kotlin, Spring Boot, and other technologies, exposing APIs for the frontend and new products while sustaining the legacy system through services, queues, continuous delivery, and operations monitoring.